Technical Field
The present disclosure relates to an event analysis apparatus, an event analysis method and a computer program product configured to analyze a cause-and-effect relationship of an event such as an alarm generated in a plant, an operator's operation and the like and to extract an improvement candidate.
Related Art
In a distributed control system configured to control a field device group such as a sensor, an actuator and the like by using a control system such as field controllers distributed and arranged in a plant, an event analysis apparatus configured to acquire and analyze events such as an alarm generated in the plant, an operator's operation and the like has been known.
In the event analysis apparatus, the number and a frequency of occurrences of each event are statistically treated, and a person in charge of operation, an operation consultant and the like of the plant acquire the information about the alarm frequently generated, the operation frequently performed and the like and perceive an entire tendency of the events to improve the operation and the safety of the plant by using the event analysis apparatus.
In recent years, a method of analyzing not only an individual event but also a relation among a plurality of different events has been suggested. For example, Patent Document 1 discloses a related art method of calculating an individual occurrence probability of each event and a simultaneous occurrence probability for each combination of the events from collected event logs, and establishing a Bayesian network on the basis of an obtained conditional probability.
Here, the Bayesian network is one of graphical models describing a cause-and-effect relationship by the probability, and is a probabilistic reasoning model representing reasoning of the complex cause-and-effect relationship by a directed graph structure and representing a relation between respective variables by a conditional probability.
FIG. 19 is an example of the Bayesian network depicting causal characteristics of events E1, E2, E3. As shown in FIG. 19, in the Bayesian network, the respective events are represented by nodes, and the nodes are connected each other by a unidirectional arrow. In the Bayesian network, a probability is assigned to each node, and a conditional probability of a node becoming an end point of the arrow in relation to a parent node is assigned to the node becoming the end point of the arrow.
In the example of FIG. 19, an occurrence probability of the event E1 is 0.1%, and an occurrence probability of the event E2 is 0.2%. Also, a conditional probability is assigned to the event E3 becoming an end point of the arrow, and when both the event E1 and the event E2 occur, an occurrence probability of the event E3 is 95%.
In this example, focusing on the event E1, the event E3, which is an end point of the arrow, is an effect-side event, and focusing on the event E3, the event E1 and the event E2, which are base points of the arrows, are cause-side events.
From the Bayesian network, it is possible to obtain not only the probability that the effect-side event E3 will occur when the cause-side event E1 occurs but also the probability that the cause-side event E1 has occurred when the effect-side event E3 occurred.
According to the event analysis apparatus disclosed in Patent Document 1, when a user designates an event to be focused, an event relating to the designated event is extracted on the basis of the event log, so that the Bayesian network is established and a cause-and-effect relationship with the related event is probabilistically displayed.
Thereby, the user can perceive the cause-and-effect relationship of the event to be focused and perform improvements on an operation sequence and an alarm setting, as required. For example, for alarm groups in which the cause-and-effect relationship highly occurs like a chain reaction, it is possible to reduce the number of times of alarm occurrence by integrally treating the alarm groups. Also, when there is a specific operation of which a cause is an occurrence of any alarm, it is possible to automate an alarm correspondence operation, for example.    [Patent Document 1] Japanese Patent Application Publication No. 2014-127093A    [Non-Patent Document 1] J. Pearl “Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference” Morgran Kaufman 1988, Chapter 2 BAYSESIAN INFERENCE    [Non-Patent Document 2] Hiroki Suyari the Bayesian network guide (1) MEDICAL IMAGING TECHNOLOGY Vol. 21 No. 4 Sep. 2003, p 315-318
As described above, it is possible to perceive the cause-and-effect relationship of the event to be focused and to perform improvements on an operation sequence and an alarm setting, as required, by using the event analysis apparatus disclosed in Patent Document 1.
According to the event analysis apparatus disclosed in Patent Document 1, the user designates the event to be focused. For this reason, when an event of an improvement target is determined in advance, it is possible to immediately determine whether an improvement is required by perceiving the cause-and-effect relationship of the event.
However, when an improvement target is not determined and it is required to find out an event of an improvement candidate in the plant, it is necessary to perceive the cause-and-effect relationship of each event and to determine whether an improvement is required. Therefore, the labor and time are required to extract the improvement candidate.